# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import unittest

import numpy as np
from transformers import AutoTokenizer

import mindspore as ms
from mindspore import mint

from mindone.diffusers import AuraFlowPipeline, AuraFlowTransformer2DModel, FlowMatchEulerDiscreteScheduler
from mindone.diffusers.utils.testing_utils import floats_tensor
from mindone.transformers import UMT5EncoderModel

sys.path.append(".")

from utils import PeftLoraLoaderMixinTests  # noqa: E402

ms.set_deterministic(True)
ms.manual_seed(0)
np.random.seed(0)


class AuraFlowLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
    pipeline_class = AuraFlowPipeline
    scheduler_cls = FlowMatchEulerDiscreteScheduler
    scheduler_classes = [FlowMatchEulerDiscreteScheduler]
    scheduler_kwargs = {}

    transformer_kwargs = {
        "sample_size": 64,
        "patch_size": 1,
        "in_channels": 4,
        "num_mmdit_layers": 1,
        "num_single_dit_layers": 1,
        "attention_head_dim": 16,
        "num_attention_heads": 2,
        "joint_attention_dim": 32,
        "caption_projection_dim": 32,
        "pos_embed_max_size": 64,
    }
    transformer_cls = AuraFlowTransformer2DModel
    vae_kwargs = {
        "sample_size": 32,
        "in_channels": 3,
        "out_channels": 3,
        "block_out_channels": (4,),
        "layers_per_block": 1,
        "latent_channels": 4,
        "norm_num_groups": 1,
        "use_quant_conv": False,
        "use_post_quant_conv": False,
        "shift_factor": 0.0609,
        "scaling_factor": 1.5035,
    }
    tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
    text_encoder_cls, text_encoder_id = UMT5EncoderModel, "hf-internal-testing/tiny-random-umt5"
    text_encoder_target_modules = ["q", "k", "v", "o"]
    denoiser_target_modules = ["to_q", "to_k", "to_v", "to_out.0", "linear_1"]

    @property
    def output_shape(self):
        return (1, 8, 8, 3)

    def get_dummy_inputs(self, with_generator=True):
        batch_size = 1
        sequence_length = 10
        num_channels = 4
        sizes = (32, 32)

        generator = np.random.default_rng(0)
        noise = floats_tensor((batch_size, num_channels) + sizes)
        input_ids = mint.randint(1, sequence_length, (batch_size, sequence_length), generator=ms.manual_seed(0))

        pipeline_inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "num_inference_steps": 4,
            "guidance_scale": 0.0,
            "height": 8,
            "width": 8,
            "output_type": "np",
        }
        if with_generator:
            pipeline_inputs.update({"generator": generator})

        return noise, input_ids, pipeline_inputs

    @unittest.skip("Not supported in AuraFlow.")
    def test_simple_inference_with_text_denoiser_block_scale(self):
        pass

    @unittest.skip("Not supported in AuraFlow.")
    def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
        pass

    @unittest.skip("Not supported in AuraFlow.")
    def test_modify_padding_mode(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
    def test_simple_inference_with_partial_text_lora(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
    def test_simple_inference_with_text_lora(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
    def test_simple_inference_with_text_lora_and_scale(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
    def test_simple_inference_with_text_lora_fused(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
    def test_simple_inference_with_text_lora_save_load(self):
        pass
